|Title:||Feature Based No-Reference Perceptual Depth Assessment Model for Mobile 3D Video Applications|
|Keywords:||No-reference, stereoscopic 3D video, Deptl, Symmetric, Asymmetric|
Depth perception is one of the most important characteristics which
separate 3D videos from traditional 2D videos. In this work, a feature
based no-reference perceptual depth assessment model has been proposed
for symmetric and asymmetric coded stereoscopic videos. This model
extracts disparity and temporal features to evaluate the perceived depth
of mobile 3D videos. The disparity feature is estimated by using block
based structural similarity index between the corresponding blocks of
left and right view and for temporal feature the jerkiness is estimated
between the consecutive frames for both left and right view. The
estimated features are then combined to give a single predicted score.
The performance of the model is verified by subjective experiment data.
The result indicates that the prediction performance of the proposed
model is satisfactory.